Linear Dimensionality Reduction

This lecture covers the concept of linear dimensionality reduction, starting with Cover's Theorem and the importance of high-dimensional spaces. It then delves into Principal Component Analysis (PCA), focusing on variance maximization and optimal linear mapping. The instructor explains how PCA can be applied to real-world datasets like MNIST and the UCI Iris dataset, as well as medical applications such as cancer genome analysis. The lecture concludes with examples of PCA in 3D face modeling and eigenfaces for image reconstruction.